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In this work we present a methodology for optimal management of brownfields that is illustrated on a real field. The approach does not depend on the particular reservoir flow simulator used, although streamline-derived information is leveraged to accelerate the optimization. The method allows one to include (non-linear) constraints (e.g. a recovery factor larger than a given baseline value), which are very often challenging to address with optimization tools. We rely on derivative-free optimization coupled with the filter method for non-linear constraints, although the methodology can also be combined with approaches that utilize exact/approximate gradients. Performance in terms of wall-clock time can be improved further if distributed-computing resources are available (the method is amenable to parallel implementation). The methodology is showcased using a real field in west Siberia where net present value (NPV) is maximized subject to a constraint for the recovery factor. The optimization variables represent a discrete time series for well bottom-hole pressure over a fraction of the production time frame. An increase in NPV of 7.9% is obtained with respect to an existing baseline. The optimization methods studied include local optimization algorithms (e.g. generalized pattern search) and global search procedures (e.g. particle swarm optimization). The controls for one injection well in the real field were actually modified according to the solution determined in this work. The results obtained suggest improvement for most economic scenarios. [ABSTRACT FROM AUTHOR]

The advances in high performance computing nowadays have increased the need of the computational resources to solve the large-scale problems. The large-scale electromagnetic and optical problems with the size of the order of 400 wavelengths in every dimension for a problem with arbitrary complex geometry structure can be solved. One of the most widespread numerical methods for Maxwell equations solving is Finite Difference Time Domain (FDTD) method. FDTD is based upon Yee lattice and Maxwell equations in integral form and differentional relations from which finite difference approximation is obtained. In this paper, IBM BlueGene/P and BlueGene/Q performance is compared to calculate the large electromagnetic problem using parallel Finite Difference Time Domain method. Introduced early EMWSolver3D was implemented on Lomonosov Moscow State University IBM BlueGene/P supercomputer. Using 1D data decomposition for the highly elongated spatial domains together with the asynchronous transfer operations between nodes of supercomputer it is allows to solve problems up to 1010 computational cells on more than a thousand nodes. Parallel implementation of FDTD algorithm based on hybrid MPI/OpenMP approach is used in this paper. The scalability of the algorithm on both BlueGene/P and BlueGene/Q systems that runs in different parallel modes is considered. Parallel processing performance comparison of both systems shows that the IBM BlueGene/Q supercomputer gives near linear speedup and the great efficiency on FDTD algorithm. [ABSTRACT FROM AUTHOR]